SHA2 family

Optimized implementation of all SHA2 functions

Overview

This package implements all SHA2 functions:

• sha256
• sha224
• sha512
• sha384
• sha512-256
• sha512-224

The API allows to hash types Blob, [Nat8] and Iter<Nat8>.

Links

The package is published on MOPS and GitHub. Please refer to the README on GitHub where it renders properly with formulas and tables.

The API documentation can be found here on Mops.

For updates, help, questions, feedback and other requests related to this package join us on:

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• Dfinity forum

Motivation

Interface

Usage

Install with mops

You need mops installed. In your project directory run:

mops init
mops add sha2

In the Motoko source file import the package as:

import Sha256 "mo:sha2/Sha256";
import Sha512 "mo:sha2/Sha512";

In you dfx.json make sure you have the entry:

"defaults": {
    "build": {
      "args": "",
      "packtool": "mops sources"
    }
  },

Example

import Sha256 "mo:sha2/Sha256";
[
Sha256.fromBlob(#sha256,""),
Sha256.fromBlob(#sha224,"")
];

import Sha512 "mo:sha2/Sha512";
[
Sha512.fromBlob(#sha512,""),
Sha512.fromBlob(#sha384,""),
Sha512.fromBlob(#sha512_224,""),
Sha512.fromBlob(#sha512_256,"")
];

Build & test

We need up-to-date versions of node, moc and mops installed. Suppose <path-to-moc> is the path of the moc binary of the appropriate version.

Then run:

git clone [email protected]:research-ag/sha2.git
mops install
DFX_MOC_PATH=<path-to-moc> mops test

Benchmarks

The benchmarking code can be found here: canister-profiling

We benchmarked this library's sha256 and sha512 against two other existing implementations, specifically these branches:

• motoko-sha2 for sha256/512: https://github.com/timohanke/motoko-sha2#v2.0.0
• crypto.mo from aviate labs for sha256 only: https://github.com/skilesare/crypto.mo#main

The benchmark was run with dfx 0.18.0 with cycle optimisation enabled and moc 0.11.0.

Time

We first measured the instructions for hashing the empty message:

method	Sha256	Sha512	mo-sha256	mo-sha512	crypto.mo
empty message	12,185	17,307	246,834	722,402	83,782
empty message	11,723	17,580	284,704	817,525	101,247
relative	1.0	1.5	24.3	69.7	8.6

We then measured a long message of 1,000 blocks and divided by the length. We provide the value per block where a block is 64 bytes for Sha256 and 128 bytes for Sha512, per byte, and relative to this libary's Sha256:

method	Sha256	Sha512	mo-sha256	mo-sha512	crypto.mo
per block	16,821	31,248	48,298	78,676	48,863
per byte	263	244	755	615	763
relative	1.0	0.93	2.87	2.34	2.90

Notes:

• All functions except crypto.mo have been measure with hashing type Blob. crypto.mo has been measured with hashing type [Nat8] because it does not offer type Blob directly.
• We measured with random input messages created by the Prng package. Measuring with a constant message such that all 0x00 or all 0xff is not a reliable way to measure and produces significantly different results.

Memory

Hashing also creates garbage. We measured the garbage created by a message of length 1,000 blocks and divided the result by the length of the message in bytes. This tells us how many bytes of garbage are produced for each byte that is hashed. For Sha256 this value is 0 which means that the garbage has size O(1). In fact, it is 1,008 bytes regardless of the message length.

method	Sha256	Sha512	mo-sha256	mo-sha512	crypto.mo
per byte	0	7.5	8.8	12.5	6.1

Notes:

• All functions except crypto.mo have been measure with hashing type Blob. crypto.mo has been measured with hashing type [Nat8] because it does not offer type Blob directly. Converting Blob to [Nat8] first will increase the value of garbage per byte by 4.
• We can see how the use of Nat64 in Sha512 requires signifantly more heap allocations than the use of Nat32 in Sha256.
• We can conclude that in Motoko it is advisable to use Sha256 over Sha512 despite the slightly higher performance per byte of Sha512.

Implementation notes

The round loops are unrolled. This was mainly motivated by reducing the heap allocations but it also reduced the instructions significantly.

Copyright

MR Research AG, 2023-2024

Authors

Main author: Timo Hanke (timohanke)

License

Apache-2.0